Indian Stock Market Forecasting Using LSTM-XGBoost and Technical Indicators

Abstract

The Indian stock market is characterized by high volatility, non-linear price behaviour, and sensitivity to macroeconomic, sectoral, and sentiment-driven factors, which limits the accuracy of traditional linear forecasting models such as ARIMA. Building on our earlier literature review and problem formulation, this paper presents the implementation and evaluation of an integrated deep learning framework for next-day closing price prediction of Indian equities. The framework combines a two-layer Long Short-Term Memory (LSTM) network with four complementary technical indicators — Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI), the 10–20 day Exponential Moving Average (EMA) crossover, and the Stochastic Oscillator — as engineered input features, and a downstream XGBoost classifier that converts the LSTM's continuous price forecast, together with the current indicator states, into discrete BUY, HOLD, or SELL trading signals with associated confidence scores. The complete pipeline is implemented as a full-stack platform (Python, Flask, MongoDB, React) that retrieves real-time NSE/BSE data through the yfinance API. The proposed model is evaluated on RELIANCE.NS using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), supported by a per-indicator ablation study and a comparison against an autoregressive (AR) linear baseline, a Support Vector Machine (SVM) regressor, and a single-indicator LSTM baseline. The proposed model achieved an RMSE of ₹28.56 and MAE of ₹23.48 (MAPE 1.71%) on the held-out test partition, achieving the lowest RMSE among all compared models, while the downstream XGBoost signal classifier achieved 91.7% accuracy on held-out BUY/HOLD/SELL labels.